Apparatus and method for diagnosing obstructive sleep apnea

ABSTRACT

An embodiment of the invention provides a method of diagnosing obstructive sleep apnea, the method comprising: acquiring a sleep sound signal comprising sounds made by a person during sleep; detecting a plurality of snore sounds in the sleep sound signal; determining a set of mel-frequency cepstral coefficients for each of the snore sounds; determining a characterizing feature for the sleep sound signal responsive to a sum of the variances of the cepstral coefficients; and using the characterizing feature to diagnose obstructive sleep apnea in the person.

RELATED APPLICATIONS

The present application is a US National Phase of PCT Application No.PCT/IB2011/053715, filed on Aug. 24, 2011, which claims the benefitunder 35 U.S.C. §119(e) of U.S. Provisional 61/377,105 filed on Aug. 26,2010, the disclosure of which is incorporated herein by reference.

TECHNICAL FIELD

Embodiments of the invention relate to detecting sleep apnea.

BACKGROUND

Obstructive sleep apnea (OSA) is a common disorder characterized byrepetitive collapse or narrowing of the upper airway passages duringsleep that impairs ventilation and disrupts sleep. Factors thatcontribute to upper airway collapse include reduced upper-airway dilatormuscle activity during sleep, specific upper-airway anatomical features,decreased end-expiratory lung volume, ventilatory control instability,and sleep-state instability. A collapse or narrowing of the airwaypassages during sleep may result in total or near total cessation ofbreathing or a partial reduction of ventilation.

Total or near total cessation of breathing that lasts at least tenseconds is referred to as “apnea”, and typically results in neurologicalarousal of the person from sleep that initiates activity to reopen theupper airway passages and reestablish breathing. A partial obstructionof the airway passages can lead to a partial reduction of normal airflowduring breathing by at least 50% for at least ten seconds, and isaccompanied by oxygen desaturation of blood by at least 4%, and/orarousal from sleep is referred to as “hypopnea”. In a vast majority ofcases OSA is accompanied by snoring, which is caused by vibration ofsoft tissue in the upper airway passages.

OSA is associated with an increased risk of cardiovascular disease,stroke, high blood pressure, arrhythmia and diabetes. Sleepfragmentation resulting from obstructive events can also increase aperson's risk of being involved in an accident, such as a drivingaccident as a result of excessive daytime sleepiness and fatigue. Oncediagnosed, a number of different therapies are available for treatingOSA. The therapies include behavioral modification training, use ofmasks for introducing a flow of pressurized air into the throat toprevent collapse of tissue in the upper airway passages, and surgery tomodify anatomical features of the airway passages that are responsiblefor OSA.

Diagnosis of OSA and determination of OSA severity are typically madewith reference to an index referred to as an apnea-hypopnea index (AHI).The index is simply a count of the number of apnea and hypopnea eventsthat a person exhibits per hour of sleep. An AHI index that is less thanabout 10 e/hr (events per hour) is usually considered clinicallyinsignificant. An AHI index between about 10 e/hr and about 30 e/hr isconsidered to indicate a moderate case of OSA, and an AHI index greaterthan about 30, is considered to indicate a severe case of OSA.

Whereas the AHI index appears simple and straightforward, determining anAHI value for a patient generally involves performing a sleep study,referred to as polysomnography, (PSG) study. PSG is a relativelycomplicated and expensive procedure carried out in a sleep laboratoryduring the patient's overnight stay in the laboratory. PSG typicallyinvolves attaching a variety of sensors to the patient's body to trackchanges that occur in a battery of physiological activities andfunctions such as brain activity, eye motion, skeletal muscleactivation, and heart rhythm during sleep. The waiting period for PSGhas been reported to be a few weeks to more than a year in the UnitedStates.

SUMMARY

An aspect of an embodiment of the invention relates to providing anon-invasive method of diagnosing presence of obstructive sleep apnea(OSA) in a person by determining an index, hereinafter an “apneadiagnosing index” (ADI), for the person responsive to detection andprocessing of snoring sounds made by the person during sleep. In anembodiment of the invention, a value for ADI is determined as a functionof a plurality of, optionally five, features F1, F2, F3, F4, and F5 thatcharacterize snoring sounds and provides an indication of OSA thatcorrelates with indications of OSA provided by the well-knownapnea-hypopnea index (AHI). Optionally, the function is a linearfunction.

An aspect of an embodiment of the invention relates to providing atleast one new feature that may be used to distinguish snoring soundsthat are indicative of presence and/or severity of OSA and to provide avalue for ADI. In an embodiment of the invention the at least one newfeature comprises “mel-cepstability”, which provides a measure ofvariance of mel-frequency cepstrum coefficients (MFCC) of snoring soundsexhibited during a sleep period. Optionally, the at least one newfeature comprises an average of variances in energy of groups of snoressounded during the sleep period. In an embodiment of the invention, theat least one feature comprises a number of groups of snores soundedduring the sleep period for which variance in group energy is greaterthan a predetermined threshold.

An aspect of an embodiment of the invention relates to providing amethod of classifying severity of OSA responsive to values of the ADI.In an embodiment of the invention, the ADI provides an indication as toseverity of OSA exhibited by a patient. Optionally, the ADI provides aclassification of snoring sounds as not indicative of OSA, indicative ofmild OSA, or indicative of severe OSA.

An aspect of an embodiment of the invention relates to providingapparatus, hereinafter referred to as an “ADITESTER”, which isrelatively easily and conveniently used, optionally in a homeenvironment, to diagnose OSA. In an embodiment of the invention,ADITESTER comprises a microphone that registers sounds generated by aperson and the person's environment during sleep and a computer systemthat processes the registered sounds to identify and process snoringsounds therein to provide a value for ADI

There is therefore provided in accordance with an embodiment of theinvention, a method of diagnosing OSA, the method comprising: acquiringa sleep sound signal comprising sounds made by a person during sleep;detecting a plurality of snore sounds in the sleep sound signal;determining a set of mel-frequency cepstral coefficients for each of thesnore sounds; determining a characterizing feature for the sleep soundsignal responsive to a sum of the variances of the cepstralcoefficients; and using the characterizing feature to diagnose OSA inthe person.

Optionally the method comprises: determining a plurality of groups ofthe snore sounds; determining a group feature for each of the groups;determining a characterizing feature for the sleep sound signalresponsive to the group features; and using the determinedcharacterizing feature for the sleep sound signal to diagnose OSA in theperson. Optionally, determining a group of snore sounds comprisesdetermining a cluster of consecutive snore sounds in the detected snoresounds for which a time delay between any two temporally adjacent snoresounds is less than or equal to a predetermined time period.Additionally or alternatively, the time period is equal to about aminute.

In an embodiment of the invention, determining a group feature for eachgroup comprises determining a measure of energy for each of the snoresounds in the group. Optionally, determining the group feature comprisesusing the determined energy measures to determine a measure of anaverage energy of the snore sounds in the group. Optionally, determiningthe group feature comprises using the measure of average energy todetermine a variance of the measures of snore sound energies for thegroup. The method optionally comprises determining the characterizingfeature of the sleep sound signal responsive to an average of thedetermined variances of the groups. Additionally or alternatively,determining a characterizing feature of the sleep sound signaloptionally comprises determining a number of groups in the sound signalfor which the variance is greater than a predetermined thresholdvariance.

In an embodiment the method comprises: determining a number of silentperiods in the sleep sound signal that are indicative of substantiallytotal suspension of breathing by the person; determining acharacterizing feature of the sleep sound signal responsive to thenumber of determined silent periods; and using the characterizingfeature to diagnose OSA in the person. The method optionally comprises:determining a pitch density for each of the plurality of snore sounds inthe sleep sound signal; determining an average pitch density for thesnore sounds; determining a characterizing feature of the sleep soundsignal responsive to the average pitch density; and using thecharacterizing feature to diagnose OSA in the person.

In an embodiment of the invention, using the characterizing sleep soundfeature to diagnose OSA comprises providing a classifier that providesan indication as to whether the person has OSA responsive to thedetermined characterizing feature of the sleep sound signal. In anembodiment of the invention, using the characterizing sleep soundfeature to diagnose OSA comprises diagnosing severity of OSA.Additionally or alternatively, the indication provided by the classifieroptionally comprises a figure of merit generated responsive to a linearfunction of the sleep sound characterizing feature. The methodoptionally comprises configuring the linear function so that the figureof merit is correlated with the apnea-hypopnia index (AHI).

There is therefore provided in accordance with an embodiment of theinvention a method of diagnosing OSA, the method comprising: acquiring asleep sound signal comprising sounds made by a person during sleep;detecting a plurality of snore sounds in the sleep sound signal;determining a plurality of characterizing features for the sleep soundsignal, the features comprising: a first feature determined responsiveto a sum of the variances of cepstral coefficients of the snore sounds;a second feature determined responsive to a measure of an average ofvariances in energies of snore sounds in groups of the snore sounds; athird feature determined responsive to a number of groups of snoresounds that have a variance in snore sounds energies greater than apredetermined variance; and using the determined features to diagnoseOSA in the person. Optionally, using the features comprises providing aclassifier that provides an indication as to whether the person has OSAresponsive to the features. Optionally, the indication comprises afigure of merit generated responsive to a linear function of thefeatures. The method optionally comprises configuring the linearfunction so that the figure of merit is correlated with theapnea-hypopnia index (AHI).

In an embodiment of the invention using the features comprisesdiagnosing severity of OSA. Optionally, the plurality of featurescomprises a fourth feature determined responsive to a number of silentperiods in the sleep sound signal that are indicative of substantiallytotal suspension of breathing by the person. In an embodiment of theinvention, the plurality of features comprises a fourth featuredetermined responsive to an average pitch density for the snore sounds.

There is further provided in accordance with an embodiment of theinvention, apparatus for diagnosing OSA, the apparatus comprising: amicrophone for acquiring a sleep sound signal of a person; and acomputer system configured to execute an instruction set that processesthe sleep sound signal in accordance with an embodiment of the inventionto diagnose OSA in the person. Optionally, the computer system is acloud based computer system.

BRIEF DESCRIPTION OF FIGURES

FIG. 1 schematically shows an ADITESTER in accordance with an embodimentof the invention; and

FIG. 2 schematically shows a flow diagram providing details of theoperation of the an ADITESTER shown in FIG. 1, in accordance with anembodiment of the invention;

DETAILED DESCRIPTION

In the following detailed description an ADITESTER in accordance with anembodiment of the invention, is schematically shown in FIG. 1 and itscomponents and operation are discussed with reference to the figure. TheADITESTER is shown being used to determine a value for ADI and therefroma diagnosis for the presence and severity of OSA for a person responsiveto snoring sounds that the person makes during sleep. Details of theoperation of the ADITESTER in generating a feature set thatcharacterizes the person's snoring sounds and determining an OSAdiagnosis for the person in accordance with an embodiment of theinvention are discussed with reference to a flow diagram shown in FIG.2.

FIG. 1 schematically shows an ADITESTER 20 operating to determinepossible presence and severity of OSA in a person 100 sleeping,optionally, in a bedroom 102 of his own house. ADITESTER 20 comprises amicrophone 22, optionally placed on a night table 104 near person 100and a computer system 24. Microphone 22 registers sounds made by theperson during sleep and sounds that are not made by the person thatreaches the microphone. Sounds that are made by the person comprise forexample, snoring sounds, breathing, coughing and voice sounds, andsounds that are produced by motion of the person, such as bed creakingand blanket rustling sounds. Sounds that are not made by the person maycomprise street sounds and sounds originating in other rooms of theperson's house that reach the bedroom and sounds made by appliances,such as a whirring sound made by an overhead fan 106 in bedroom 102.Sounds not made by the person may also include sounds made by anotherperson (not shown) in the bedroom. For convenience of presentation,sounds that are registered by microphone 22 that are not respiratorysounds (snoring and breathing sounds) made by person 100 are referred toas background sounds. Microphone 22 transmits the sounds that itregisters as a “sleep sound signal” schematically represented by awaveform 23, to computer system 24.

Computer system 24 processes the sleep sound signal to identify snoringsounds therein and provide a value for ADI for the person responsive tothe snoring sounds. The computer system is optionally configured havingcomputer executable instruction sets referred to as a snore detector 25,a feature extractor 26, and an ADI/OSA modeler 27 and optionallycomprises a memory 28 in which it stores sleep sound signal 23 that thecomputer system receives from microphone 22. Snore detector 25 processesthe sleep sound signal stored in the memory to identify snoring soundstherein. Feature extractor 26 processes snoring sounds identified bysnore detector 25 to determine features, in accordance with anembodiment of the invention, that characterize the snoring sounds andmay be used to determine a value for ADI for person 100. ADI/OSA modeler27 uses the features provided by the feature extractor to determine avalue for ADI and therefrom a diagnosis as to presence and severity ofOSA for person 100.

Computer system 24 may comprise a smart phone PC, a laptop, and/or awork book located in the home of person 100 that stores and executes theinstruction sets defining snore detector 25, feature extractor 26, andADI/OSA modeler 27. However, computer system 24 is not limited to beinghoused in a single computer, or a computer located in a same room withperson 100. Computer system 24 may be a distributed system havingcomponents and executable instruction sets located in different servers,and may be partially or completely based on access to servers via theinternet, that is partially or completely “cloud based”. For example,memory 28 may be located close to microphone 22 and directly coupled tothe microphone to receive and store sleep sound signal 23. Snoredetector 25, extractor 26 and ADI/OSA modeler may be connected to memory28 and each other by the internet and reside and function in differentinternet servers.

Aspects and functioning of ADITESTER 20, snore detector 25, featureextractor 26, and ADI/OSI modeler 27 in determining if person 100suffers from OSA, and if so a severity of the OSA, are discussed belowwith reference to a flow diagram 200 shown in FIG. 2A.

In a block 202 ADITESTER 20 is turned on and microphone 22 isregistering sounds made in or reaching room 102 and transmitting analogelectronic signals that form sleep sound signal 23 to computer system24. The computer system converts sound signal 23 from an analog signalto a digital signal and optionally stores the digital sleep sound signalin memory 28. Hereinafter, unless otherwise specified, reference tosleep sound signal 23 is assumed to reference the digital form of thesleep sound signal. Sleep sound signal 23 includes background sounds,such as background sounds noted above, and respiratory sounds made byperson 100 during a period in which the person is asleep. A sleepperiod, for which an associated sleep sound signal 23 is acquired, mayhave different durations, and may of course have duration of a nominalfull night's sleep of 6-8 hours. The sleep sound signal may includeelectromagnetic interference from power lines and appliances in aneighborhood of ADITESTER 20.

In a block 204, snore detector 25 processes sleep sound signal 23 todistinguish and identify snoring sounds in the sound signal. Any ofvarious methods and algorithms may be used by the snore detector toidentify snoring sounds. In an embodiment of the invention, snoredetector 25 first filters sleep sound signal 23 to remove readilyidentifiable interference, such as electromagnetic interferencegenerated at frequencies of alternating currents in power lines andappliance transformers, from sleep sound signal 23.

Thereafter, snore detector 25 processes the filtered sleep sound signal23 to identify portions, hereinafter referred to as “audio events”, ofthe filtered sleep sound signal 23 having energy and duration thatindicate that the audio events are candidates for being “snore signals”,which represent snoring sounds made by person 100. In an embodiment ofthe invention, for a portion of sleep sound signal 23 to be consideredan audio event, the portion may be required to exhibit energy greaterthan a determined threshold energy E_(th) and have a duration “τ_(d)”greater than a minimum duration τ_(dmin) and less than a maximumduration τ_(dmax).

To determine a value for E_(th) the sleep sound signal is segmented intoconsecutive, optionally partially overlapping sound frames havingduration equal to about 30 ms (milliseconds). An energy for each soundframe is optionally, determined to be equal to a sum of squaredamplitudes of sleep sound signal 23 in the frame, or an average of thesquared amplitudes in the frame.

In an embodiment of the invention a value for E_(th) is determined foreach of a plurality of relatively long “windows” of time into whichsleep sound signal 23 is divided responsive to lower and upper boundenergies E_(L) and E_(U) respectively determined for sleep sound signal23. A time window may have duration equal to hundreds or thousands oftimes that of the sound frames into which sleep signal 23 is segmented.Optionally E_(L) and E_(U) are determined from a frequency distributionof frames in the sleep sound signal as a function of frame energy. In anembodiment of the invention, E_(L) is an energy greater than an energyat which the distribution is maximum and for which the distributionfalls to a fraction of the maximum. Optionally, the fraction is equal toabout 0.10. Optionally, E_(U) is a multiple of E_(L) determined toprovide a reasonable upper limit to a value determined for E_(th).

For a given time window, a candidate threshold energy “CE_(th)” forthreshold energy E_(th) of the window is determined from a frequencydistribution of frames in the window as a function of frame energy.Optionally, CE_(th) is an energy equal to a factor times an energygreater than an energy at which the frequency distribution is maximum,and for which the distribution falls to a fraction of its maximum.Optionally, the factor is equal to about 1.3. Optionally, the fractionis equal to about 0.10.

In an embodiment of the invention, the threshold energy E_(th) for thegiven time window is set equal to E_(L) if CE_(th)<E_(L); is set equalto E_(U) if CE_(th)>E_(U); and is set equal to CE_(th) ifE_(L)≦CE_(th)≦E_(U).

A portion of sleep sound signal 23 in a given time window is determinedto be an audio event in accordance with an embodiment of the inventionif the portion comprises a plurality of consecutive sound frames: thathave cumulative duration τ_(d) satisfying the constraintτ_(dmin)≦τ_(d)≦τ_(dmax); that have a peak energy “E_(p)” greater thanE_(th); and for which none of the frames have energy less than athreshold energy “E_(r)”. In an embodiment of the invention, E_(r) isequal to 0.5(E_(th)+E_(Wm)), where E_(Wm) is a minimum energy exhibitedby frames in the given window. Optionally, τ_(dmin), is equal to about0.2 s (seconds) and τ_(dmax) is equal to about 2.5 s.

For each audio event that is determined to be a candidate snore signal,snore detector 25 generates a feature set and uses a Gaussian mixturemodel (GMM) classifier to determine responsive to the feature set, ifthe candidate snore signal is to be classified as a snore signal.

In an embodiment of the invention the feature set that snore detector 25generates for a snore signal candidate comprises a set of “n” linearpredictor coefficients (LPC), and the candidate's pitch density; averagepitch value; total energy; duration, and rise time. Optionally, n isequal to 12. In an embodiment of the invention the GMM classifiercomprises two Gaussian density models, one having order n_(S) for snoresignal candidates that represent snoring sounds and one having ordern_(B) for snore signal candidates that represent background sounds.Optionally, n_(S) and n_(B) are equal to 3 and 11, respectively. A setof GMM parameters that define the GMM classifier are optionallydetermined as a GMM parameter set that maximizes a likelihood of thefeature sets for the models. The feature sets acquired for each of aplurality of training snore signal candidates that are known torepresent a snoring sound or a background sound.

In a block 206 snore signals identified by snore detector 25 in sleepsound signal 23 acquired for person 100 are processed by featureextractor 26 to define a feature set for sleep sound signal 23 that maybe used to provide a value for ADI and therefrom a diagnosis of OSA forthe person, in accordance with an embodiment of the invention. In anembodiment of the invention, feature extractor 26 generates a featureset comprising five features, F1, F2, F3, F4, and F5, for sleep soundsignal 23.

Feature F1, referred to as a “Mel-Cepstability” of sleep sound signal23, is a function of mel-frequency cepstral coefficients (MFCC)determined from the log power spectra as functions of frequency measuredin the mel-frequency scale of snore signals identified by snore detector25 in sleep sound signal 23.

The mel scale is a perceptual scale of frequencies, measure in “mels”,that maps frequency conventionally measured in Hz to a perceptual scalefor which pairs of pitches having a same difference in mels areperceived by a human as having a same difference in frequency, or pitch.A frequency of 1000 Hz has a value in mels equal to 1000. A frequency“f_(Hz)” in Hz has a frequency f_(mel) in mels defined by a formula:f _(mel)=2595 log 10(1+f _(Hz)/700).  1)

Let an s-th snore signal of a total of “S” snore signals identified insleep sound signal 23 acquired for person 100 have a time dependentamplitude represented by A_(s)(t). Then a power spectrum P(f_(Hz)) ofA_(s)(t) as a function of frequency in Hz may be written:P(f _(Hz))=|F{A _(s)(t)}|²,  2)where F{A_(s)(t)} is a Fourier transform of A_(s)(t). FilteringP(f_(Hz)) with a mel-frequency filterbank comprising K mel-frequencyfilters, provides a discrete mel-frequency power spectrum P(k,f_(mel))for A_(s)(t) having K values.P(k,f _(mel))=(MEL_(k) |F{A _(s)(t)}≡²), k=1→K.  3)IfX _(k)(s)={log(MEL_(k) |F{A _(s)(t)}|²)}, k=1→K,  4)then a discrete cosine transform (DCT) of the X_(k)(s) generatesoptionally K mel-frequency cepstral coefficients c_(i)(s) for A_(s)(t),wherec _(i)(s)=Σ_(k=1) ^(k=K) X _(k)(s)cos [i(k−½)π/K], i=1→K.

In accordance with an embodiment of the invention, feature F1, that isMel-Cepstability, is a sum of the variances of the MFCC c_(i)(s)optionally normalized to an average energy “E” of the S snore signals insleep sound signal 23 acquired for person 100. In symbols, F1 may bedefined by an expression,F1=MelCepstability=Σ_(s=1) ^(s=S)Σ_(i=1) ^(i=K)[c _(i)(s)− c _(i)(s)]²/E.  6)

In equation 6) c _(i)(s) is an average value for c_(i)(s) over all Ssnore signals identified in sleep sound signal 23, and is defined by anexpression,c _(i)(s)=Σ_(s=1) ^(s=S) c _(i)(s), i=1→K.  7)If E(s) is the energy of the s-th snore signal thenE(s)=Σ_(k=1) ^(k=K) X _(k)(s)², and the average snore energy E may bewritten  8)E=(1/S)Σ_(s=1) ^(s=S) E(s).  9)

Feature F2 is optionally equal to an average of variances in energy forgroups of snore signals in sleep sound signal 23. A group of snoresignals in accordance with an embodiment of the invention comprises asequence of snores in sleep sound signal 23 for which a time delaybetween an end of a snore in the group and a next subsequent snore inthe group is less than or equal to a maximum time lapse “τ_(g)”.Optionally, 37 τ_(g) is equal to one minute.

Assume that sleep sound signal 23 comprises “G” snore signal groups, anda g-th group contains “S_(g)” snore signals. If the variation in snoresignal energy in a given group g is varE(g), an average energy of snoresignals in the group is Ē(g), and an s-th snore signal in the group hasenergy E (s, g), thenvarE(g)=Σ_(s=sg) ^(s=Sg)[E(s, g)−Ē(g)]² /S _(g),  10)and if an average of varE(g) is varE(g), thenF2=varE(g)=(1/G)Σ_(g=1) ^(g=G)varE(g).  11)

In an embodiment of the invention, feature F3 is equal to a number ofsnore groups in sleep sound signal 23 whose variance, varE(g), in thegroup energy is greater than a threshold variance, “varE(g)_(TH).” Insymbols,F3=Σ_(g=1) ^(g=G)bool{varE(g)>varE(g)_(TH) }/G.  12)

Feature F4 is a count N_(Q) of a number of silent periods, referred to a“quiet hiatuses”, which are indicative of substantially total suspensionof breathing in sleep sound signal 23 that are located between two audioevents, whether or not at least one of the audio events is classified asa snore signal. In accordance with an embodiment of the invention, to beconsidered a silent period an absence of sound from person 100 isrequired to have duration “τ_(Q)” greater than a minimum duration“τ_(Qmin)” and less than a maximum duration equal to “τ_(Qmax)”. Insymbols, if A(t)_(S) the time dependent amplitude of sleep sound signal23, the F4 count N_(Q) of quiet hiatuses may be defined by anexpression:F4=N _(Q)=Σ_(s=1) ^(s=S)bool{(τ_(Qmin)<τ_(Q)<τ_(Qmax))|A _(S)(t)≦A_(SB)}  13)In an embodiment of the invention τ_(Qmin) is equal to about 10 secondsand τ_(Qmax) is equal to about 90 seconds. A_(SB) is substantially equalto a background noise level that may exist when person 100 is not makingany respiratory sounds.

Feature F5 is optionally equal to a mean of the pitch density of allsnore signals identified in sleep sound signal 23 acquired for person100. In an embodiment of the invention a pitch density for an s-th snoresignal is determined by segmenting the snore signal into “F” frameshaving duration equal to 30 ms (milliseconds) and determining a maximumof an autocorrelation function for each frame. The pitch density PD(s)for the s-th snore signal is equal to a fraction of the frames in thesnore signal for which a maximum of the autocorrelation function isgreater than a threshold value “R”. If the autocorrelation function of agiven “f-th” frame in the s-th snore signal is represented by Rii(s,f)and a number of frames in the snore signal is equal to F, then,PD(s)=Σ_(f=1) ^(f=F)bool{MaxRii(s,f)>R}/F,  14)and if an average of PD(s) over all snore sounds in sleep sound signal23 is PD, thenF5=PD=Σ_(s=1) ^(s=S)PD(s)/S.  15)

In a block 208 ADI/OSA calculator 27 processes the features F1 . . . F5to generate a value for ADI. In an embodiment the invention, ADI isdetermined as a linear function of the features in accordance with anequation:ADI=α_(o)+α₁ F1+α₂ F2+α₃ F3+α₄ F4+α₅ F5.  16)

Optionally coefficients α_(o) . . . α₅ are determined to provide a bestfit to measurements of AHI acquired for a training group of persons thatincludes persons who do not exhibit OSA and persons who exhibit OSAcharacterized by different degrees of severity. Optionally values forAHI for the persons are determined from PSG studies. A best fit isoptionally determined by a least squares analysis.

By way of a numerical example, features F1, F2, F3, F4, and F5 asdefined above may assume values in the following ranges:F1: [0 to 0.5], F1: [0 to 1.5], F1: [0 to 1], F1: [0 to 500], and F1: [0to 1].  17)Best fit values for α_(o) . . . α₅ determined from a training group ofabout 90 people may have values,α_(o)=−3, α₁=128.1, α₂=18.8, α₃=14.9, α₄=0.0075, and α₅=−48.0143.  18)

ADI determined in accordance with equation 16) for the numerical valuesgiven in expressions 17) and 18) was found to be able to distinguishwhether a person had: no or a clinically insignificant case of OSA; amild case of OSA (AHI index greater than 10 and less than 30); or asevere case of OSA (AHI index greater than 30). Classification of OSAusing ADI in accordance with an embodiment of the invention was found toagree well with classifications provided by values of AHI determined byPSG.

The confusion matrix below indicates correlation between the ADI indexdetermined in accordance with an embodiment of the invention and an AHIdetermined by PSG. Rows in the table are labeled with a diagnosis ofOSA, “NO OSA”, “MILD OSA”, or “SEVERE OSA”, determined by PSG. For eachrow, a diagnosis of OSA determined in accordance with the ADI index isgiven in columns headed “NO OSA”, “MILD OSA”, or “SEVERE OSA”. From thematrix it is seen that the ADI and AHI indices give a same diagnosis 87%of the time for people with no OSA and 84% of the time for people withsevere OSA. For mild cases of OSA agreement falls to about 56% but thetwo indices will agree 78% of the time as to whether or not a person hasOSA.

ADI INDEX NO MILD SEVERE OSA OSA OSA AHI INDEX NO 0.87 0.10 0.03 OSAMILD 0.22 0.56 0.22 OSA SEVERE 0.05 0.11 0.84 OSA

It is noted that whereas in the above description of embodiments of theinvention, a linear regression function is used to provide a value ofADI and a diagnosis of OSA, practice of embodiments is not limited tolinear regression classifiers. Non linear regression functions, supportvector functions of F1 . . . F5, and any of various other regressionmethods may be used in accordance with an embodiment of the invention todetect and classify cases of OSA.

In the description and claims of the present application, each of theverbs, “comprise” “include” and “have”, and conjugates thereof, are usedto indicate that the object or objects of the verb are not necessarily acomplete listing of components, elements or parts of the subject orsubjects of the verb.

Descriptions of embodiments of the invention in the present applicationare provided by way of example and are not intended to limit the scopeof the invention. The described embodiments comprise different features,not all of which are required in all embodiments of the invention. Someembodiments utilize only some of the features or possible combinationsof the features. Variations of embodiments of the invention that aredescribed, and embodiments of the invention comprising differentcombinations of features noted in the described embodiments, will occurto persons of the art. The scope of the invention is limited only by theclaims.

The invention claimed is:
 1. A method of diagnosing obstructive sleepapnea (OSA), the method comprising: acquiring a sleep sound signalcomprising sounds made by a person during sleep; detecting a pluralityof snore sounds in the sleep sound signal; determining a set ofmel-frequency cepstral coefficients for each of the snore sounds;determining a variance for each of the sets of mel-frequency cepstralcoefficients; summing the determined variances; determining acharacterizing feature for the sleep sound signal responsive to the sumof the variances of the cepstral coefficients; and using thecharacterizing feature to diagnose OSA in the person, wherein thecharacterizing feature comprises a numerical value used as an indicationof severity of OSA, where a low value means no OSA or a clinicallyinsignificant case of OSA, a medium value means mild OSA, and a highvalue means severe OSA.
 2. A method according to claim 1 and comprising:determining a plurality of groups of the snore sounds: determining agroup feature for each of the groups; determining a characterizingfeature for the sleep sound signal responsive to the group features; andusing the determined characterizing feature for the sleep sound signalto diagnose OSA in the person.
 3. A method according to claim 2 whereindetermining a group of snore sounds comprises determining a cluster ofconsecutive snore sounds in the detected snore sounds for which a timedelay between any two temporally adjacent snore sounds is less than orequal to a predetermined time period.
 4. A method according to claim 2wherein the time period is equal to about a minute.
 5. A methodaccording to claim 2 wherein determining a group feature for each groupcomprises determining a measure of energy for each of the snore soundsin the group.
 6. A method according to claim 5 wherein determining thegroup feature comprises using the determined energy measures todetermine a measure of an average energy of the snore sounds in thegroup.
 7. A method according to claim 6 wherein determining the groupfeature comprises using the measure of average energy to determine avariance of the measures of snore sound energies for the group.
 8. Amethod according to claim 7 and comprising determining thecharacterizing feature of the sleep sound signal responsive to anaverage of the determined variances of the groups.
 9. A method accordingto claim 7 wherein determining a characterizing feature of the sleepsound signal comprises determining a number of groups in the soundsignal for which the variance is greater than a predetermined thresholdvariance.
 10. A method according to claim 1 and comprising: determininga number of silent periods in the sleep sound signal that are indicativeof substantially total suspension of breathing by the person;determining a characterizing feature of the sleep sound signalresponsive to the number of determined silent periods; and using thecharacterizing feature to diagnose OSA in the person.
 11. A methodaccording to claim 1 and comprising: determining a pitch density foreach of the plurality of snore sounds in the sleep sound signal;determining an average pitch density for the snore sounds; determining acharacterizing feature of the sleep sound signal responsive to theaverage pitch density; and using the characterizing feature to diagnoseOSA in the person.
 12. A method according to claim 1 wherein using thecharacterizing sleep sound feature to diagnose OSA comprises providing afigure of merit as to whether the person has OSA that is a linearfunction of the sleep sound characterizing feature.
 13. A methodaccording to claim 12 and comprising configuring the linear function sothat the figure of merit is correlated with an apnea-hypopnia index(AHI).
 14. A method of diagnosing OSA, the method comprising: acquiringa sleep sound signal comprising sounds made by a person during sleep;detecting a plurality of snore sounds in the sleep sound signal;determining a set of mel-frequency cepstral coefficients for each of thesnore sounds; determining a variance for each of the sets ofmel-frequency cepstral coefficients; summing the determined variances;determining a plurality of characterizing features for the sleep soundsignal, the features comprising: a first feature determined responsiveto the sum of the variances of cepstral coefficients of the snoresounds; a second feature determined responsive to a measure of anaverage of variances in energies of snore sounds in groups of the snoresounds; a third feature determined responsive to a number of groups ofsnore sounds that have a variance in snore sounds energies greater thana predetermined variance; and using the determined features to diagnoseOSA in the person, wherein the plurality of characterizing features areused to obtain a numerical value used as an indication of severity ofOSA, where a low value means no OSA or a clinically insignificant caseof OSA, a medium value means mild OSA, and a high value means severeOSA.
 15. A method according to claim 14 wherein the indication comprisesa figure of merit generated responsive to a linear function of thefeatures.
 16. A method according to claim 15 and comprising configuringthe linear function so that the figure of merit is correlated with anapnea-hypopnia index (AHI).
 17. A method according to claim 14 whereinthe plurality of features comprises a fourth feature determinedresponsive to a number of silent periods in the sleep sound signal thatare indicative of substantially total suspension of breathing by theperson.
 18. A method according to claim 14 wherein the plurality offeatures comprises a fourth feature determined responsive to an averagepitch density for the snore sounds.
 19. Apparatus for diagnosing OSA,the apparatus comprising: a microphone for acquiring a sleep soundsignal of a person; and a computer system configured to: detect aplurality of snore sounds in the sleep sound signal; determine a set ofmel-frequency cepstral coefficients for each of the snore sounds;determine a variance for each of the sets of mel-frequency cepstralcoefficients; summing the determined variances; determine acharacterizing feature for the sleep sound signal responsive to the sumof the variances of the cepstral coefficients; and use thecharacterizing feature to diagnose OSA in the person, wherein thecharacterizing feature comprises a numerical value used as an indicationof severity of OSA, where a low value means no OSA or a clinicallyinsignificant case of OSA, a medium value means mild OSA, and a highvalue means severe OSA.